kalman filtering deconvolution based on maximum a posteriori estimation

a study on the chinese continuous speech recognition unsupervised speaker adaptation based on maximum a posteriori algorithm

基于MAP算法的无教师讲者自适应的研究

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maximum a posteriori probability detection for free-space optical communication

自由空间光通信的最大后验概率检测

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hidden markov model adaptation algorithm using maximum a posteriori nonlinear transformation in noisy environments

噪声环境下基于最大后验非线性变换的隐马尔可夫模型自适应算法

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the application of maximum a posteriori in image blind deconvolution

最大后验方法在图像复原中的应用

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the location disparity was estimated based on maximum a posteriori criterion.

基于极大后验概率估计准则计算了位置偏差的估值。

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study of image reconstruction in electrical impedance tomography using the algorithm based on maximum a posteriori

阻抗听力计，阻坑测听仪基于极大后验估计理论的电阻抗断层成像重建算法研究

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channel compensation of speaker identification based on maximum a posteriori

说话人识别中基于最大后验概率的通道补偿方法

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particle filter tracking algorithm based on maximum a posteriori

基于最大后验概率密度的粒子过滤器跟踪算法

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maximum a posteriori restoration with markov constraint for three-dimensional optical-sectioning microscopy

基于马尔科夫约束最大后验概率三维显微图像复原算法

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simplified log maximum a posteriori algorithm

一种简化的对数最大后验概率译码算法

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secondly, in view of huge computational cost in spectrum peak searching of traditional music algorithm, by combining metropolis-hastings sampler of markov chain monte carlo ( mcmc) and sequential searching by eliminating correlativity, a fast 2d-doa estimation algorithm based on arbitrary array is proposed. 4.

其次，针对传统MUSIC算法在二维DOA估计中谱峰搜索运算量巨大的问题，通过结合马尔可夫链蒙特卡罗的MH抽样算法与去相关逐次搜索的方法，给出一种基于任意阵形的快速二维DOA估计算法。

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before the joint antenna and sub-carriers allocation, the antenna selection based on markov chain monte carlo ( mcmc) is executed to reduce the quantity of space-frequency blocks used to resource allocation. consequently, the computational complexity is reduced enormously while retaining the advantage of space diversity.

在天线子载波联合分配之前，利用基于MCMC（Markov chain Monte Carlo）的天线选择算法减少参与联合分配的空频资源块，在尽量保留空间分集优势的情况下大大降低计算复杂度。

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in this study, probabilistic back analysis and model calibration for unsaturated soil slopes under rainfall condition are conducted based on the bayesian theory adopting the markov chain monte carlo simulation method. firstly, a new probabilistic method is proposed for back analysis of slope failure.

本文基于贝叶斯理论，采用马尔可夫链蒙特卡罗方法，针对降雨入渗条件下土坡稳定性问题，对非饱和土坡进行了参数反分析和模型校准研究。

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firstly, the classical improved algorithm of markov chain monte carlo particle filter is improved. the improved algorithm brings in the concept of effective particles, abandons the degenerate particles at proper time, and adjusts the number of particle dynamically.

首先对经典的改进算法马尔可夫蒙特卡罗粒子滤波算法进行改进，引入有效粒子数概念，在适当的情况下，抛弃退化的粒子，动态的调整粒子数目以减小计算量。

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on the basis of above, we proposed the quantile regressive risk measurement model, conducted the identification of the model based on the reversible jump markov chain monte carlo method, and estimated parameters in the bayesian framework.

在此基础上，本文构建了分位回归风险测度模型，利用逆跳马尔可夫链蒙特卡罗算法对模型进行识别，并对分位回归风险测度模型进行贝叶斯参数估计。

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the algorithm uses the particle filter with the extended kalman filter ( ekf) proposal distribution, fuses two measurements, and incorporates the residual resampling step and the markov chain monte carlo methods.

该算法使用扩展卡尔曼滤波提议分布的粒子滤波，融合双站测量数据，包含了残差重抽样步骤以及马尔可夫链蒙特卡罗法等技巧。

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based on the demographic parameters of life table of the cotton bollworm, helicoverpa armigera ( h ü bner), its population dynamics was simulated using markov chain monte carlo method.

依据对棉铃虫自然种群生命表研究所获得的种群统计参数，采用蒙特卡罗方法，模拟了随机环境条件下的棉铃虫种群发生动态。

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simulation results show that the estimation performance of this algorithm is superior to that of traditional particle filter and particle filter with the introduction of mcmc ( markov chain monte carlo).

仿真结果表明，该算法估计性能优于传统粒子滤波器和引入马尔可夫链蒙特卡罗MCMC（Markov chain Monte Carlo）的粒子滤波器。

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the results show that the reversible jump markov chain monte carlo method can correctly identify the model, and the bayesian quantile autoregressive risk measurement model can accurately estimate var.

研究结果表明，逆跳马尔可夫链蒙特卡罗法能准确识别模型，贝叶斯分位回归风险模型能对风险价值进行准确估计。

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reversible jump markov chain monte carlo ( rjmcmc) algorithm is proposed for performing low-dimensional coordinate and choice of dimension simultaneously within the bayesian framework.

在贝叶斯框架下提出一种可逆转跳马尔可夫链蒙特卡罗（RJMCMC）的算法，能在形成多维尺度分析的低维主坐标的同时决定本质维数。

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